Prompt Engineering

Fit Large Codebase Context — Code Tokenizes Differently

Code is denser in tokens than prose: symbols, indentation, and short identifiers all cost extra. Estimate code files with code ratios before pasting them.

Overview

The most common surprise in code-heavy prompts: a file that "looks small" consumes far more tokens than the same character count of prose, because tokenizers split symbols, casing, and indentation aggressively. This scenario loads a real reconciliation module and shows the engine's content-type detection at work — the text is classified as Code and estimated with code ratios, not prose ratios. The budget math then answers the practical questions: how many files of this size fit alongside the question, and how much room the review's answer needs.

Workflow

Let detection classify it
Braces, arrows, and indentation flip the estimate to code ratios automatically — no manual setting.
Budget per file
One file's estimate scales linearly — the headroom line says how many more fit.
Reserve review-sized output
Code questions get long answers; a Large response budget keeps the review from truncating.

Why This Works

Code-aware ratios correct the systematic underestimate prose math produces
Automatic type detection removes the setting nobody knows how to choose
Per-file budgeting matches how code conversations actually grow

Best for

Code review and refactoring prompts with pasted sources
Developers feeding multiple files into one conversation
Anyone surprised by code's token appetite

Not for

Reviewing the pasted code's quality — that's the Code Review Prompt Generator
Packaging the files with delimiters and labels — that's the Long Input Formatter

Use cases

Budgeting how many files fit in a review prompt
Estimating a module before pasting it for analysis
Explaining why code "runs out of context" faster than prose

Customize This Resource

Opens this scenario in Context Window Estimator. Estimate to get the full context budget report — then adjust the model and response budget.

Open in Context Window Estimator

Prompt Template

CONTEXT BUDGET REPORT

INPUT ANALYSIS
- Characters: 42,073
- Words: 4,950
- Paragraphs: 55
- Detected content type: Code
- Estimated tokens: ~13,357 (range 11,687–15,027)

MODEL & BUDGET
- Target model: Claude Opus — 200,000 token context window
- Reserved response budget: 16,000 tokens (Large Response)
- Available input budget: 184,000 tokens

FIT VERDICT: SAFE
- The input uses an estimated 6–8% of the available input budget.

BUDGET BREAKDOWN
- Context window:          200,000 tokens
- Reserved for response:   -16,000 tokens
- Available for input:     184,000 tokens
- Estimated input:         ~11,687–15,027 tokens
- Remaining headroom:      168,973–172,313 tokens

GUIDANCE
- The content fits comfortably — even the high end of the estimate uses half the budget or less.
- No action needed. There is ample room for follow-up turns in the same conversation.

MODEL COMPARISON
The same content and response budget across supported models:
- GPT-5 (400K window): SAFE — ~3–4% of available budget
- Claude Sonnet (200K window): SAFE — ~6–8% of available budget
- Claude Opus (200K window): SAFE — ~6–8% of available budget
- Gemini Pro (1049K window): SAFE — ~1–1% of available budget

NOTE
- Token figures are character-based estimates, not tokenizer output — actual counts vary by model and content.
- Model windows verified June 2026. Provider limits change; check current documentation before relying on the edge of a budget.

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